AIMC Topic: Metals

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Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm.

Sensors (Basel, Switzerland) Mar 17, 2020
The detection of objects concealed under people's clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to ...
Metals Limit of Detection Neural Networks, Computer Feasibility Studies Safety Management Sensitivity and Specificity Algorithms Deep Learning Humans Computer Systems Clothing Image Interpretation, Computer-Assisted Crime Datasets as Topic Electromagnetic Phenomena Security Measures Weapons
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Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Medical image analysis Jan 30, 2020
Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are ...
Supervised Machine Learning Neural Networks, Computer Motion Tomography, X-Ray Computed Artifacts Metals Radiographic Image Interpretation, Computer-Assisted Pacemaker, Artificial Humans
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A dual-stream deep convolutional network for reducing metal streak artifacts in CT images.

Physics in medicine and biology Nov 26, 2019
Machine learning and deep learning are rapidly finding applications in the medical imaging field. In this paper, we address the long-standing problem of metal artifacts in computed tomography (CT) images by training a dual-stream deep convolutional n...
Neural Networks, Computer Machine Learning Algorithms Tomography, X-Ray Computed Humans Image Processing, Computer-Assisted Prostheses and Implants Artifacts Pedicle Screws Reproducibility of Results Deep Learning Metals Proton Therapy Visible Human Projects
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Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.

Medical physics Sep 13, 2019
PURPOSE: In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is of...
Artifacts Tooth Humans Neural Networks, Computer Image Processing, Computer-Assisted Prostheses and Implants Metals Cone-Beam Computed Tomography
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Metal artifact reduction for the segmentation of the intra cochlear anatomy in CT images of the ear with 3D-conditional GANs.

Medical image analysis Sep 4, 2019
Cochlear implants (CIs) are surgically implanted neural prosthetic devices that are used to treat severe-to-profound hearing loss. These devices are programmed post implantation and precise knowledge of the implant position with respect to the intra ...
Humans Tomography, X-Ray Computed Metals Neural Networks, Computer Radiographic Image Enhancement Imaging, Three-Dimensional Algorithms Cochlea Artifacts Cochlear Implants
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A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection.

Interdisciplinary sciences, computational life sciences Sep 4, 2019
BACKGROUND: Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces.
Corrosion Static Electricity Benzimidazoles Chemistry Linear Models Conservation of Natural Resources Quantitative Structure-Activity Relationship Metals Imaging, Three-Dimensional Computational Biology Machine Learning Predictive Value of Tests Support Vector Machine Algorithms Hydrazines Cluster Analysis
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Unsupervised learning of a deep neural network for metal artifact correction using dual-polarity readout gradients.

Magnetic resonance in medicine Aug 12, 2019
PURPOSE: A new unsupervised learning method was developed to correct metal artifacts in MRI using 2 distorted images obtained with dual-polarity readout gradients.
Phantoms, Imaging Humans Neural Networks, Computer Algorithms Reproducibility of Results Magnetic Resonance Imaging Models, Statistical Unsupervised Machine Learning Deep Learning Artifacts Metals Image Processing, Computer-Assisted
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ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.

IEEE transactions on medical imaging Aug 5, 2019
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underly...
Image Processing, Computer-Assisted Artifacts Machine Learning Tomography, X-Ray Computed Metals Humans Neural Networks, Computer
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Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Molecular informatics Apr 12, 2019
Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which v...
Deep Learning Metals Proteins Binding Sites
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Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain.

Food chemistry Jan 19, 2019
Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. o...
Oryza Electronic Nose Plant Proteins Uric Acid Principal Component Analysis Animals Linear Models Weevils Oxides Metals Edible Grain Fuzzy Logic
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